Morphological Inflection
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Morphological Inflection is the task of generating a target (inflected form) word from a source word (base form), given a morphological attribute, e.g. number, tense, and person etc. It is useful for alleviating data sparsity issues in translating morphologically rich languages. The transformation from a base form to an inflected form usually includes concatenating the base form with a prefix or a suffix and substituting some characters. For example, the inflected form of a Finnish stem eläkeikä (retirement age) is eläkeiittä when the case is abessive and the number is plural.
Source: Tackling Sequence to Sequence Mapping Problems with Neural Networks
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Probing Subphonemes in Morphology Models
Can a Neural Model Guide Fieldwork? A Case Study on Morphological Data Collection
OOVs in the Spotlight: How to Inflect them?
Exploring Linguistic Probes for Morphological Generalization
Morphological Inflection with Phonological Features
An Investigation of Noise in Morphological Inflection
Morphological Inflection: A Reality Check
Understanding Compositional Data Augmentation in Typologically Diverse Morphological Inflection
A Framework for Bidirectional Decoding: Case Study in Morphological Inflection
Autoregressive Modeling with Lookahead Attention
An Extended Sequence Tagging Vocabulary for Grammatical Error Correction
Modeling the Graphotactics of Low-Resource Languages Using Sequential GANs
Eeny, meeny, miny, moe. How to choose data for morphological inflection
A Comprehensive Comparison of Neural Networks as Cognitive Models of Inflection
UniMorph 4.0: Universal Morphology
Systematic Inequalities in Language Technology Performance across the World's Languages
A Three Step Training Approach with Data Augmentation for Morphological Inflection
Rule-based Morphological Inflection Improves Neural Terminology Translation
(Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models' Performance
Falling Through the Gaps: Neural Architectures as Models of Morphological Rule Learning
Minimal Supervision for Morphological Inflection
Can a Transformer Pass the Wug Test? Tuning Copying Bias in Neural Morphological Inflection Models
On Biasing Transformer Attention Towards Monotonicity
Do RNN States Encode Abstract Phonological Processes?
Smoothing and Shrinking the Sparse Seq2Seq Search Space
Searching for Search Errors in Neural Morphological Inflection
Linguistically inspired morphological inflection with a sequence to sequence model
The NYU-CUBoulder Systems for SIGMORPHON 2020 Task 0 and Task 2
SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection
Applying the Transformer to Character-level Transduction
Mind Your Inflections! Improving NLP for Non-Standard Englishes with Base-Inflection Encoding
Learning to Learn Morphological Inflection for Resource-Poor Languages
A Resource for Studying Chatino Verbal Morphology
A Latent Morphology Model for Open-Vocabulary Neural Machine Translation
The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.